This project's goals are to increase knowledge of the epidemiology and cost of medically identified suicide acts and to provide tools to further increase knowledge at the state and national level. A medically identified suicide act is a suicide act that was medically treated or fatal and that someone coding injury cause (assigning an external cause or E-code) probably would identify as a suicide act. E-coding is mandated for all injury deaths, hospital discharges in 23 states, and emergency department (ED) discharges in 8 states but is voluntary elsewhere. The project aims: (1 ) To compare demographic, method choice, and other characteristics of suicide completers versus hospital admitted attempters, to compute age-adjusted completion rates by mechanism, and to analyze medically identified comorbidities associated with hospital-admitted suicide acts. This analysis will use pooled E-coded mortality and hospital discharge data from 15-20 states, and in a separate analysis, state ED data as available. (2) Using the pooled state data and CHAID tree-building tools, to develop and validate a discriminant analysis model for estimating injury causes from national or state hospital discharge data without E-codes. To apply the model to national hospital discharge data (E-coding is voluntary), then combine the output with national mandatory-E-code data on mortality, ED discharges, and physician visits. This activity will yield the first reliable, detailed picture of the epidemiology of medically identified suicide acts in the US. (3) Applying probit and logit analyses to the pooled state data, to assess the influence of state level iaws and policies on odds that hospital-admitted or fatal injury results from a suicide act and that a suicide act is completed. Among others, variables considered will include coroner versus medical examiner state, suicide coding practices and state prevention programming (variables derived from telephone interviews with in-state experts), mental health care parity level, substance abuse, health insurance coverage mandates, existence of advanced treatment directives, and percentage of households with guns. (4) To develop and apply a probit based model for assigning intent to the 3,000-3,500 injury deaths per year that are coded as intent unknown. The model will use medical examiner data from Maryland and North Carolina and the pooled state data. We expect the model to suggest the estimated annual US suicide death toll is 5%-7% above reported levels. (5) To modify an existing injury cost model to more accurately cost suicide acts and apply the model to national data.